Determinant social factors influencing obesity among Malay

Opción, Año 36, Regular No.91 (2020): 249-262 ISSN 1012-1587/ISSNe: 2477-9385

Recibido: 20-12-2019 •Aceptado: 20-02-2020

Determinant social factors influencing obesity

among Malay people

Norizan Abdul Ghani1

1Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin,

21300, Kuala Terengganu, Malaysia.

[email protected]

Saif Ullah2

2Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin,

21300, Kuala Terengganu, Malaysia.

[email protected]

Atif Amin Baig3

3Faculty of Medicine, Universiti Sultan Zainal Abidin, 20400, Kuala

Terengganu, Malaysia.

[email protected]

Jumadil Saputra4

4School of Social & Economic Development, University Malaysia

Terengganu, 21030, Kuala Terengganu, Malaysia.

[email protected]

M. Umair Khan5

5Faculty of Economics and Management Sciences, Universiti Sultan

Zainal Abidin, 20400, Kuala Terengganu, Malaysia.

[email protected]

Abstract

This study has explored the determinant social factors of obesity

among Malay obese people. Multiple regressions analysis (stepwise

method) was performed to identify and examine the most influenced

factors that contribute to obesity. The analysis determined that eating

habits, physical activity barriers, and feelings (low self-esteem) are

dominating social factors. However, among these factors eating habits

were observed as the most influencing predictor in the model. In

conclusion, this pioneering study is providing baseline data of

influencing factors of obesity in Malaysia, so future researches may be

conducted based on the given results to reduce the obesity trend.

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

250 Norizan Abdul Ghani et al. Opción, Año 36, Regular No.91 (2020): 249-262

Keywords: Social factors, Influencing factors, Obesity, Malay

race.

Factores sociales determinantes que influyen en la

obesidad entre los malayos

Resumen

Este estudio ha explorado los factores sociales determinantes de

la obesidad entre las personas obesas malayas. Se realizó un análisis de

regresiones múltiples (método por pasos) para identificar y examinar

los factores más influenciados que contribuyen a la obesidad. El

análisis determinó que los hábitos alimenticios, las barreras de

actividad física y los sentimientos (baja autoestima) son factores

sociales dominantes. Sin embargo, entre estos factores, los hábitos

alimenticios se observaron como el predictor más influyente en el

modelo. En conclusión, este estudio pionero está proporcionando datos

de referencia de los factores que influyen en la obesidad en Malasia,

por lo que se pueden realizar investigaciones futuras basadas en los

resultados dados para reducir la tendencia a la obesidad.

Palabras clave: Factores sociales, Factores de influencia,

Obesidad, Raza malaya.

1. INTRODUCTION

Obesity worldwide considers a health issue, and its prevalence

increased in both developed and developing countries. Furthermore, it

is also related to physical and mental problems in an individual and

community (DJALALINIA, 2015). Obesity develops a substantial

adverse impact on health status. The increased ratio of obesity will

lead to cardiovascular diseases and poor health. Malaysian ranked

higher in both overweight and obese categories, which may lead to a

higher mortality rate besides the economic burden of the country.

Determinant social factors influencing obesity among malay people 251

Globally, 3.4 million people have died due to the problem of weight

gain and obesity in 2010.

It was explicitly noted that obesity had significantly increased

the death risk, but overweight had a blurred image (FLEGAL,

GRAUBARD, WILLIAMSON & GAIL, 2005). A Nationwide survey

conducted by Institute of Public Health has revealed that 2.6 million

(15.22%) Malaysian adult people have diabetes problem and around

5.8 million (32.7%) have hypertension, and this figure is rising in next

national survey where 3.5 million (17.7%) people are diabetics, and

9.6 million (47.7%) of the adult population has hypertension. Further,

Tan explained that in Malaysia, three out of five deaths occurred due

to obesity-related health problems.

Further, studies have shown the indirect costs due to obesity in

terms of absenteeism (CAWLEY, RIZZO & HAAS, 2007) reduce the

productivity presenteeism (SCHMIER, JONES & HALPERN, 2006),

injuries at the workplace (POLLACK & CHESKIN, 2007), and

disability payments (RICCI & CHEE, 2005). In Australia, the

estimated monetary value was up to $A8.28 -13824 in 2008 that

includes the care costs $1.9billion (23%), loss of productivity $3.6

billion and health system $2.0 billion (24%). Interestingly the facts

showed that a 1% reduction in the pervasiveness of BMI would be cost

savings of $US 43 million in 2030, and $US 85 million in 2050

(RTVELADZE, MARSH, BARQUERA, ROMERO, LEVY,

MELENDEZ & BROWN, 2014). Therefore, the measures would be in

need for the interventions towards obesity concerning to reduce the


health care costs (AHMAD & AHMAD, 2019; RTVELADZE ET AL.,

2014). Malaysia also suffers the highest cost for obesity as a

percentage of the country's healthcare spending, reaching an alarming

to 10-20%. Among other ASEAN countries, Indonesia ranks second

with costs at 8-16% of healthcare spending, while Singapore comes

third, with costs at 3-10%.

Most researchers tend to involved in genetics and nutrients parts

for the interventions of obesity, but there is a lack of knowledge

regarding social factors associated with obesity in Malaysia especially

in Kuala Terengganu. SAUNDERS (2012) determined that social

factors have a critical role in human weight gain. Further, Ullah

explained the correlation between various social factors. The nominal,

ordinal and scale based items were used to gather the data. According

to the study, feelings (low self-esteem), body image dissatisfaction,

eating habits, physical activity, physical activity barriers, and media

influence were significantly associated with obesity. However, this

study did not mention the most determinant factors of obesity among

the Malay race. There is also a need to identify the most determinant

social factors influencing obesity among Malay obese people which

will cover in this present study.

2. METHODOLOGY

This cross-sectional sectional was carried out among Malay

obese people residing in Kuala Terengganu. 150 obese samples were


decided to include in the study. According to ABDOLLAHI & ABU

TALIB (2016), the minimum sample size required for the study is 100

respondents (COHEN, MANION & MORRISON, 2000; AHMAD &

AHMAD, 2018). However, While, BABBIE (1990) mentioned that

60% of the response ratio would be necessary for the research

specifically in surveys to reduce the level of non-response bias

(FINCHAM, 2008; AHMAD & SAHAR, 2019). Among 150 obese

Malay people, 80 (53.3%) were males, and 70 (46.7%) were females.

Further, the convenience sampling method was undertaken to

recruit the participants. The inclusion criteria were (1) Obese aged 20

to 59 years old (BMI >27.49 Kg/m2 ), (2) Respondent who were

willing to participate voluntarily, (3) Respondent can read, write and

understand English and Bahasa Melayu, (4) Both Male and Female

obese individuals and (5) Malay ethnicity. For exclusion criteria, (1)

Obese aged below 20 and more than 59 years old (BMI < 27.49 Kg/m2

), (2) Respondent who were not willing to participate, (3) Respondent

who cannot read, write and understand questionnaire in Bahasa

Melayu and English very well, (4) Pregnant respondent, and (5) Non-

Malay ethnicity.

BMI of participants was obtained by given guidelines of

Clinical Practice Guidelines on Management of Obesity. All the

calculations were recorded in a standard formation, and the BMI was

classified according to the weight status of an individual.


Data collection was conducted from June 2017 to November

2017. The information was gathered from UniSZA (Gong Badak

Campus and medical campus), Universiti of Malaysia Terengganu, and

Sultan Nur Zahirah Hospital. The participants were approached who

fulfilled the inclusion criteria and willing to participate voluntarily for

this study. The researcher explained the objective of the research to the

participants and obtained verbal consent. The response rate was

90.5%. Further, data was entered into SPSS version 23.0 Multiple

regressions analysis (stepwise method) was performed to explore the

most influenced social factor among the population.

Table 1: Classification of BMI in Asian Adults given by Clinical

Practice Guidelines on Management of Obesity

BMI Weight Status

18.5 – 22.9 Normal and Healthy Weight

23.00 - 27.49 Overweight

27.50 - 34.99 Obese I

(35.00 - 39.99) Obese II

>40 kg/m2 Obese III

Data collection was conducted from June 2017 to November

2017. The information was gathered from UniSZA (Gong Badak

Campus and medical campus), Universiti of Malaysia Terengganu, and

Sultan Nur Zahirah Hospital. The participants were approached who

fulfilled the inclusion criteria and willing to participate voluntarily for

this study. The researcher explained the objective of the research to the

participants and obtained verbal consent. The response rate was

90.5%. Further, data was entered into SPSS version 23.0. Multiple


regressions analysis (Stepwise method) was performed to explore the

most influenced social factor among the population.

3. RESULTS

Table 2 shows the regression analysis of the model summary of

ANOVA and coefficients. It shows that the regression model has been

performed at three steps. It means, three most important and

significant variables were identified and correspondingly R-Square

(coefficient of determination) mentioned in Table 2. R-square showed

that at three steps, using three variables, 43.2% variation could explain

in the dependent variable.

Table 2: Model Summary of Eating habits, Physical Activity Barriers,

& Feelings (low self-esteem) on Obesity

Model R R Square Adjusted R

Square

Std. The

error of the

Estimate

1 0.556a 0.309 0.304 3.256

2 0.640b 0.410 0.402 3.019

3 0.657c 0.432 0.420 2.976

a. Predictors: (Constant), eating habits

b. Predictors: (Constant), eating habits, physical activity barriers

c. Predictors: (Constant), eating habits, physical activity barriers,

feelings

(low self-esteem)

d. Dependent Variable: Obesity


The result of ANOVA as per given in Table 3 indicated that F

value was significant at F (37.034), p < 0.0.5.

Table 3: ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 702.032 1 702.032 66.232 0.000b

Residual 1568.740 148 10.600

Total 2270.772 149

2 Regression 931.439 2 465.719 51.116 0.000c

Residual 1339.333 147 9.111

Total 2270.772 149

3 Regression 981.272 3 327.091 37.034 0.000d

Residual 1289.499 146 8.832

Total 2270.772 149

Eating habits, physical activity barriers, and feelings (low self-

esteem) were the most significant factors respectively. All these three

variables have p-values < 0.05. Beta coefficients showed the individual

impact of variables on the dependent variable. The regression

coefficient value, its sign, and corresponding p-value are observed.

Except for feelings (low self-esteem), the other two variables have a

significantly positive role in increasing obesity of subjects. Feelings

(low self-esteem) have an average negative impact on the dependent

variable. It explained that a person having low self-esteem would

increase the level of obesity and vice versa. Standardised coefficients

have shown that eating habits are the most strong predictor in the final

model.


Table 4: Test of Hypothesis (Regression Coefficients)

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std.

Error

Beta

1 (Constant) 19.288 2.085 9.252 0.000

Eating

habits

0.451 0.055 0.556 8.138 0.000

2 (Constant) 15.879 2.049 7.751 0.000

Eating

habits

0.344 0.056 0.424 6.186 0.000

Physical

activity

barriers

0.216 0.043 0.344 5.018 0.000

3 (Constant) 20.579 2.825 7.284 0.000

Eating

habits

0.324 0.055 0.400 5.861 0.000

Physical

activity

barriers

0.229 0.043 0.365 5.362 0.000

Feelings

(low self-

esteem)

-0.090 0.038 -0.150 -

2.375

0.019

Dependent Variable: Obesity

Table 5 indicates that body image dissatisfaction (β = .127, p >

0.05), physical activity (β = -.045, p > 0.05), and media influence (β =

-.141, p > 0.05) were found to be insignificant and were excluded

accordingly.


Table 5: Excluded Variables (Stepwise-Based)

Model Beta In t Sig.

1 Feelings (low self-

esteem)

-0.106b -1.551 0.123

Body image

dissatisfaction

0.312b 4.547 0.000

Physical activity -0.105b -1.528 0.129

Physical activity

barriers

0.344b 5.018 0.000

Media influence -0.095b -0.689 0.492

2 Feelings (low self-

esteem)

-0.150c -2.375 0.019

Body image

dissatisfaction

0.123c 1.147 0.253

Physical activity -0.056c -0.854 0.394

Media influence -0.193c -1.510 0.133

3 Body image

dissatisfaction

0.127d 1.204 0.231

Physical activity -0.045d -0.694 0.489

Media influence -0.141d -1.096 0.275

4. DISCUSSION

The findings of this study asserted the determinant factors of

obesity. The regression analysis (Stepwise method) eliminated the

variables such as body image dissatisfaction, physical activity and

media influence in the final model, yet these factors cannot be

neglected. From the results, it can be determined that eating habits,

physical activity barriers, and feelings (low self-esteem) are the most

dominant factors which are influencing obesity.


However, eating habits are performing vital their role in order to

increase the obesity level. There may be a change of eating outside

frequently among Malay people. A study conducted by Lim revealed

that people are dining out 13 times per week in Malaysia. Another

reason may be explained that Malay people preferred to consume

sweet drinks and beverages instead of water during meals. Factors

such as working mothers, food varieties (both local and international),

sweet drinks and beverages served at many premises encouraged the

practice of eating-out. Restaurants, night markets (Pasar Malam), food

courts and food stalls were servicing not only those who wanted to eat

at meal times but also those who wanted to enjoy food with

friends/family members in a festive and relaxed manner. Food caterers

were also available to serve at formal functions (meetings, seminars) in

offices and home during religious and family occasions.

Income inequalities may actuate differences in lifestyle, for

example, exercise, eating habits, and cigarette smoking. National

Health and Morbidity Survey determined the lower level of vegetable

utilization in men with a low salary than that in men with a high salary.

Fruit and meat utilization was lesser in participants with lower income

than that in participants with high income among both sexes.

Therefore, lack of consideration of nutrient contents, irregular eating

time, poor food quality and premises’ cleanliness might expose the

practitioner to health, social, familial and even safety risks.

Moreover, physical activity barriers described their influence on

obesity. It shows how obese Malay people are experiencing physical


activity barriers which are leading to obesity. To decrease the obesity

trend, the state government should observe the barriers at the personal

and social level when creating interventional programs to promote

physical activity. Therefore, understanding physical activity barriers is

very important.

Moreover, third variable feelings (low self-esteem) also showed

it significant importance affecting obesity which may be due to the

mediating role of body image dissatisfaction which compels an

individual towards unhealthy dieting or emotional eating. As obesity is

a rising issue and distressing the Malay obese people, so eating habits

are worth evaluation among several factors to cure obesity problems.

The government should come up with interventional programs that

will cover these neglected factors to avoid people from obesity.

Further, more researches are expected to clarify whether feelings (low

self-esteem) and physical activity barriers interact with eating habits or

not among Malay obese people.

5. CONCLUSION

This pioneering study has evaluated the strong influence of

eating habits on obesity. Other determinant social factors were

physical activity barriers and feelings (low self-esteem). There is a

need to inculcate good habits of healthy eating and regular physical

exercise among Malay obese people. Also, initiatives to promote

exercise may also need to focus on physical activity barriers among the


population. This study is providing a baseline data of influencing

factors in Malaysia and emphasized researchers to conduct further

studies by aligning the given results to decrease the burden of obesity

from the country.

REFERENCES

ABDOLLAHI, A., & ABU TALIB, M. 2016. “Self-esteem, body-

esteem, emotional intelligence, and social anxiety in a college

sample: The moderating role of weight”. Psychology, health &

medicine. Vol. 21, No 2: 221-225. UK.

AHMAD, I., & AHMAD, S. 2018. “Multiple Skills and Medium

Enterprises’ Performance in Punjab Pakistan: A Pilot Study”.

Journal of Social Sciences Research. Vol. 7: 44-49.

AHMAD, I., & AHMAD, S. 2019. “The Mediation Effect of Strategic

Planning on The Relationship between Business Skills and

Firm’s Performance: Evidence from Medium Enterprises in

Punjab, Pakistan”. Opcion. Vol. 35, No 24: 746-778.

AHMAD, I., & SAHAR. 2019. “Waste Management Analysis From

Economic Environment Sustainability Perspective”.

International Journal of Scientific & Technology Research. Vol. 8, N

o 12: 1540-1543.

BABBIE, E. 1990. Survey Research Methods. Belmont, California:

Wadsworth Publishing Company, Inc. USA.

CAWLEY, J., RIZZO, J., & HAAS, K. 2007. “Occupation-specific

absenteeism costs associated with obesity and morbid obesity”.

Journal of Occupational and Environmental Medicine. Vol.

49, No 12: 1317-1324. UK.

COHEN, L., MANION, L., & MORRISON, K. 2000. Research

methods in education (5th ed.). London: Routledge/ Falmer.

UK.

DJALALINIA, S., QORBANI, M., PEYKARI, N., & KELISHADI, R.

2015. “Health impacts of obesity”. Pakistan journal of

medical sciences. Vol. 31, No 1: 239. Pakistan.


FINCHAM, J. 2008. “Response rate responsiveness for surveys,

standards and the journal”. American Journal of

Pharmaceutical. Vol. 72: p. 13. USA.

FLEGAL, K., GRAUBARD, B., WILLIAMSON, D., & GAIL, M.

2005. “Excess deaths associated with underweight, overweight,

and obesity”. JAMA. Vol. 293: 1861–1867. USA.

POLLACK, K., & CHESKIN, L. 2007. “Obesity and workplace

traumatic injury: does the science support the link?” Injury

Prevention. Vol. 13, No 5: 297-302. UK.

RICCI, J., & CHEE, E. 2005. “Lost productive time associated with

excess weight in the US workforce”. Journal of Occupational

and Environmental Medicine. Vol. 47, No 12: 1227-1234.

USA.

RTVELADZE, K., MARSH, T., BARQUERA, S., ROMERO, L.,

LEVY, D., MELENDEZ, G., & BROWN, M. 2014. “Obesity

prevalence in Mexico: impact on health and economic burden”.

Public health nutrition. Vol. 17, No 1: 233-239. UK.

SAUNDERS, M. 2012. Social factors in childhood and adulthood

associated with adult obesity in African American and white

women. ISRN Public Health. UK.

SCHMIER, J., JONES, M., & HALPERN, M. 2006. “Cost of obesity

in the workplace”. Scandinavian journal of work,

environment & health: 5-11. Scandinavia.

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Año 36, N° 91 (2020)

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Determinant social factors influencing obesity among Malay